Human sleep is generally described as a succession of five recurring stages (plus waking, which is sometimes classified as a sixth stage). Sleep stages are typically monitored using a polysomnograph to collect physiological signals from the sleeping subject, including brain waves (EEG), eye movements (EOG), muscle activity (EMG), heartbeat (ECG), blood oxygen levels (SpO2) and respiration. The commonly-recognized stages include:                Stage 1 sleep, or drowsiness. The eyes are closed during Stage 1 sleep, but if aroused from it, a person may feel as if he or she has not slept.        Stage 2 is a period of light sleep, during which the body prepares to enter deep sleep.        Stages 3 and 4 are deep sleep stages, with Stage 4 being more intense than Stage 3.        Stage 5, REM (rapid eye movement) sleep, is distinguishable from non-REM (NREM) sleep by changes in physiological states, including its characteristic rapid eye movements.Polysomnograms show brain wave patterns in REM to be similar to Stage 1 sleep. In normal sleep, heart rate and respiration speed up and become erratic, while the muscles may twitch. Intense dreaming occurs during REM sleep, but paralysis occurs simultaneously in the major voluntary muscle groups.        
Although sleep staging is most often performed by a human operator, who reads and scores the polysomnogram, there are also methods known in the art for computerized sleep staging. Penzel et al review such methods in “Computer Based Sleep Recording and Analysis,” Sleep Medicine Reviews 4:2 (2000), pages 131-148, which is incorporated herein by reference. According to this article, the minimum requirements for digital polysomnography as a basis for automatic sleep scoring include measurement of EEG, EOG and EMG, along with respiratory, cardiovascular and movement-related parameters.
Although automated sleep-staging is typically based primarily on analysis of the EEG signal, ECG analysis is frequently used along with the EEG to provide complementary information. For example, Telser et al. describe a method for detecting sleep transitions using ECG signals in “Can One Detect Sleep Stage Transitions for On-Line Sleep Scoring by Monitoring the Heart Rate Variability?” Somnologie 8 (2004), pages 33-41, which is incorporated herein by reference. The authors state that analysis of heart rate variability (HRV) can be used to distinguish NREM sleep from REM and wakefulness, but cannot distinguish between wakefulness and REM.